Memorial Sloan-Kettering Cancer Center Nomogram Research Articles

External validation of the Memorial Sloan Kettering Cancer Center preoperative nomogram predicting lymph node invasion in a cohort of high-grade prostate cancer patients.

Commonly used preoperative nomograms predicting clinical and pathological outcomes in prostate cancer (PCa) patients have not been yet validated in high-grade only PCapatients. Our objective is to perform an external validation of the Memorial Sloan Kettering Cancer Center (MSKCC) preoperative nomogram as a predictor of lymph node invasion (LNI) in a cohort of high-grade PCa patients. We included patients with high-grade PCa (Gleason ≥8) treated at our institution between 2011 and 2020 with radical prostatectomy and pelvic lymph node dissection without receiving neoadjuvant or adjuvant therapy. The area under the curve (AUC) of the receiver operator characteristic (ROC) was used to quantify the accuracy of the model to predict LNI. A calibration plot was used to evaluate the model's precision, and a decision curve analysiswas computed to evaluate the net benefit associated with its use. This study was approved by our institution's ethics board. A total of 242 patients with a median age of 66 (60-71) years were included. LNI was observed in 70 (29%) patients with a mean of 16 (median = 15; range = 2-42) resected nodes. The MSKCC nomogram discriminative accuracy, as evaluated by the AUC-ROC was 79.0% (CI: [0.727-0.853]). The MSKCC preoperative nomogram is a good predictor of LNI and a useful tool associated with net clinical benefit in this patient population.

Population-Based Validation of the MIA and MSKCC Tools for Predicting Sentinel Lymph Node Status

Patients with melanoma are selected for sentinel lymph node biopsy (SLNB) based on their risk of a positive SLN. To improve selection, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models, but the utility of these models remains to be tested. To determine the clinical utility of the MIA and MSKCC models. This was a population-based comparative effectiveness research study including 10 089 consecutive patients with cutaneous melanoma undergoing SLNB from the Swedish Melanoma Registry from January 2007 to December 2021. Data were analyzed from May to August 2023. The predicted probability of SLN positivity was calculated using the MSKCC model and a limited MIA model (using mitotic rate as absent/present instead of count/mm2 and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The clinical utility of each model was assessed using decision curve analysis and compared with a strategy of performing SLNB on all patients. Among 10 089 included patients, the median (IQR) age was 64.0 (52.0-73.0) years, and 5340 (52.9%) were male. The median Breslow thickness was 1.8 mm, and 1802 patients (17.9%) had a positive SLN. Both models were well calibrated across the full range of predicted probabilities and had similar external area under the receiver operating characteristic curves (AUC; MSKCC: 70.8%; 95% CI, 69.5-72.1 and limited MIA: 69.7%; 95% CI, 68.4-71.1). At a risk threshold of 5%, decision curve analysis indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all patients. The greatest benefit was observed in patients with T2 melanomas using a threshold of 10%; in that setting, the use of the nomograms led to a net reduction of 8 avoidable SLNBs per 100 patients for the MSKCC nomogram and 7 per 100 patients for the limited MIA nomogram compared to a strategy of SLNB for all. This study confirmed the statistical performance of both the MSKCC and limited MIA models in a large, nationally representative data set. However, decision curve analysis demonstrated that using the models only improved selection for SLNB compared to biopsy in all patients when a risk threshold of at least 7% was used, with the greatest benefit seen for T2 melanomas at a threshold of 10%. Care should be taken when using these nomograms to guide selection for SLNB at the lowest thresholds.

The Integrated i31-GEP Test Outperforms the MSKCC Nomogram at Predicting SLN Status in Melanoma Patients.

Sentinel lymph node biopsy (SLNB) for patients with cutaneous melanoma is primarily a prognostic procedure that broadly identifies patients who may have disease progression and may warrant additional intervention. However, 88% of patients undergoing SLNB receive a negative result and of those, some will succumb to their disease. One clinical utility of the integrated 31-GEP test, which combines gene expression data with clinicopathologic factors to provide a personalized, precise risk of SLN positivity, is SLNB guidance. This study compared the i31-GEP for SLNB to a nomogram that predicts SLN positivity using only clinicopathologic factors. Patients with T1-T2 tumors and known SLN status (N=465) were analyzed by the i31-GEP for SLNB and a nomogram developed at Memorial Sloan Kettering Cancer Center (MSKCC). A 5% risk threshold was used to conform with national guidelines. In patients with <5% predicted risk, SLN positivity was 2.7% (3/111) for i31-GEP versus 10.0% (11/110, p=0.026) for MSKCC. In each T-category, the i31-GEP maintained a false-negative rate below the 5% risk threshold in those predicted to have a <5% risk, while the MSKCC nomogram did not. Integrating the 31-GEP with traditional factors outperformed a nomogram that uses clinicopathologic factors alone to predict SLN status. Incorporating the i31-GEP into clinical practice could improve identification of patients for SLNB, resulting in better risk-aligned management.

Diagnostic performance of a novel radiomic model for predicting post-treatment prostate cancer recurrence: A comparison to CAPRA and MSKCC nomograms.

76 Background: mpMRI-derived radiomic features have been shown to capture sub-visual patterns for quantitative characterization of tumor phenotype. We seek to compare the diagnostic performance of a mpMRI-based radiomic model to currently available nomograms for prediction of post-radical prostatectomy (RP) biochemical recurrence (BCR). Methods: mpMRI was obtained from 76 patients who had underwent RP for treatment of localized PCa. All patients had ≥2 years follow-up and those with neo-adjuvant or adjuvant treatment were excluded. Radiomic analysis and cross-validation of mpMRI features yielded features significantly correlated with BCR, defined as two consecutive serum PSA≥0.2ng/ml. These features were aggregated to construct a radiomic model, which was compared to the risk scores generated by inputting patients’ clinicodemographic features into the USCF Cancer of the Prostate Risk Assessment (UCSF-CAPRA) score and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical Prostatectomy nomogram. The performance of each model was compared utilizing receiver-operator curve (ROC) analysis and area under the curve (AUC) was reported. Results: In feature extraction and ranking, six radiomic features were determined to be important and non-redundant in predicting PCa recurrence (least material condition, gray-level non-uniformity, shape-elongation, shape-sphericity, first-order skewness). These features were aggregated into the radiomic model and repeated five-fold cross validation yielded a model with AUC of 0.95±0.06, 33% sensitivity, and 100% specificity. UCSF-CAPRA and MSKCC nomograms yielded AUC of 0.72±0.07 and 0.82±0.07, respectively. Conclusions: The mpMRI-derived radiomic model performed well when compared to the UCSF-CAPRA score and MSKCC Pre-Radical Prostatectomy nomogram. Future projects will incorporate patient demographics and disease characteristics available at the time of initial PCa diagnosis to improve the radiomic model accuracy.

Ipsilateral tumor recurrence risk in women with ductal carcinoma in situ: application of the Van Nuys Prognostic Index and the Memorial Sloan Kettering Cancer Center nomogram.

To apply the Van Nuys Prognostic Index (VNPI) and the Memorial Sloan Kettering Cancer Center (MSKCC) ductal carcinoma in situ (DCIS) nomogram to DCIS patients with known long-term outcomes. A retrospective review was performed of consecutive patients diagnosed with DCIS from 2007 to 2014. Included patients underwent breast-conserving surgery (BCS) and were followed with imaging for at least five years. For each patient, the VNPI and MSKCC nomogram risk estimates were determined. In addition, variables used in both models were compared between women with and without recurrences using the Wilcoxon signed-rank test and the Pearson's chi-squared test. Over the eight-year period, 456 women (average age 57 years, range 30-87) underwent BCS for DCIS. Thirty-one (6.8%) experienced an ipsilateral recurrence. The average VNPI scores were 7 (range 5-9) and 7 (range 4-10) for women with and without a recurrence (p = 0.14), respectively, with 4-6, 7-9, and 10-12 being the low, moderate, and high-risk groups, respectively. Per the MSKCC nomogram, the average five-year recurrence risks were 5% (range 1-12%) and 4% (range 1-38%) for women with and without a recurrence (p = 0.09), respectively. The recurrence risk-related variables were younger patient age, need for one or more re-excision surgeries, and use of endocrine therapy for 0 to less than five years after surgery. Ipsilateral tumor recurrence risk estimates based on the VNPI and MSKCC nomogram are similar between women with DCIS who did and did not have a recurrence, suggesting that more robust prognostic models are needed.

Population-based validation of the melanoma institute australia (MIA) and the memorial sloan-kettering cancer center (MSKCC) predictive tool for sentinel node status in patients with melanoma.

9571 Background: Patients with primary cutaneous melanoma are selected for sentinel lymph node biopsy (SLNB) based upon their risk of a positive SLN being identified. To improve upon stage-based categorization, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models. Which model is more clinically useful is currently unclear. Methods: Consecutive patients undergoing SLNB from 2007-2021 were identified from the Swedish Melanoma Registry (SweMR), which covers 99% of patients with invasive cutaneous melanoma in Sweden. The predicted probability of SLN positivity was calculated using the MSKCC and limited MIA models (using mitotic rate as absent/present instead of count and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The “clinical utility” of each model was assessed using decision curve analysis (DCA) and compared with a strategy of performing SLNB on all patients. Results: In total 10,089 patients were included; the median Breslow thickness was 1.8 mm, 33.7% were ulcerated, and 1,802 patients had a positive SLN (17.9%). Both the MSKCC and limited MIA models were well calibrated across the full range of predicted probabilities, and had similar external area under curve (AUC), MSKCC 70.8% (95% CI 69.5-72.1%) and MIA 70.3% (95% CI 68.9-71.7%). When new models were constructed to compare the incremental value of the additional parameters of either nomogram over Breslow thickness alone, the AUCs were 70.1%, 71.6% and 72.0% for Breslow alone, MIA parameters and MSKCC parameters, respectively. At a risk threshold of 5%, DCA indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all. The greatest benefit was observed from use of the models in patients with T2 melanomas when a threshold of 10% was used to select patients for SLNB. In that setting, use of the nomograms led to a net reduction in seven avoidable SLNBs per 100 patients for the limited MIA nomogram and eight for the MSKCC nomogram, when compared to a strategy of SLNB for all. Conclusions: This study confirms the statistical performance of both the MSKCC and limited MIA models in a large, nationally-representative dataset. However, clinical utility as assessed by DCA demonstrated that using the models only improved selection for SLNB when a risk threshold of at least 10% was employed, and then mainly for T2 melanomas. Either further development of the nomograms, or other strategies, are needed to improve selection at lower risk thresholds.

Analysis of Local Recurrence Risk in Ductal Carcinoma In Situ and External Validation of the Memorial Sloan Kettering Cancer Center Nomogram.

Adjuvant radiotherapy and hormonotherapy after breast-conserving surgery (BCS) in ductal carcinoma in situ (DCIS) have been shown to reduce the risk of local recurrence. To predict the risk of ipsilateral breast tumor relapse (IBTR) after BCS, the Memorial Sloan Kettering Cancer Center (MSKCC) developed a nomogram to analyze local recurrence (LR) risk in our cohort and to assess its external validation. A historical cohort study using data from 296 patients treated for DCIS at the Hospital Clínic of Barcelona was carried out. Patients who had had a mastectomy were excluded from the analysis. The mean age was 58 years (42-75), and the median follow-up time was 10.64 years. The overall local relapse rate was 13.04% (27 patients) during the study period. Actuarial 5- and 10-year IBTR rates were 5.8 and 12.9%, respectively. The external validation of the MSKCC nomogram was performed using a multivariate logistic regression analysis on a total of 207 patients, which did not reach statistical significance in the studied population for predicting LR (p = 0.10). The expression of estrogen receptors was significantly associated with a decreased risk of LR (OR: 0.25; p = 0.004). In our series, the LR rate was 13.4%, which was in accordance with the published series. The MSKCC nomogram did not accurately predict the IBTR in this Spanish cohort of patients treated for DCIS (p = 0.10).

Development and External Validation of a Novel Nomogram to Predict the Probability of Pelvic Lymph-node Metastases in Prostate Cancer Patients Using Magnetic Resonance Imaging and Molecular Imaging with Prostate-specific Membrane Antigen Positron Emission Tomography

BackgroundPreoperative assessment of the probability of pelvic lymph-node metastatic disease (pN1) is required to identify patients with prostate cancer (PCa) who are candidates for extended pelvic lymph-node dissection (ePLND). ObjectiveTo develop a novel intuitive prognostic nomogram for predicting pathological lymph-node (pN) status in contemporary patients with primary diagnosed localized PCa, using preoperative clinical and histopathological parameters, magnetic resonance imaging (MRI), and prostate-specific membrane antigen (PSMA) positron emission tomography (PET). Design, setting, and participantsIn total, 700 eligible patients who underwent robot-assisted radical prostatectomy and ePLND were included in the model-building cohort. The external validation cohort consisted of 305 surgically treated patients. Logistic regression with backward elimination was used to select variables for the Amsterdam-Brisbane-Sydney nomogram. Outcome measurements and statistical analysisPerformance of the final model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses. Models were subsequently validated in an external population. Results and limitationsThe Amsterdam-Brisbane-Sydney nomogram included initial prostate-specific antigen value, MRI T stage, highest biopsy grade group (GG), biopsy technique, percentage of systematic cores with clinically significant PCa (GG ≥2), and lymph-node status on PSMA-PET. The AUC for predicting pN status was 0.81 (95% confidence interval [CI] 0.78–0.85) for the final model. On external validation, the Amsterdam-Brisbane-Sydney nomogram showed superior discriminative ability to the Briganti-2017 and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms (AUC 0.75 [95% CI 0.69–0.81] vs 0.67 [95% CI 0.61–0.74] and 0.65 [95% CI 0.58–0.72], respectively; p < 0.05), and similar discriminative ability to the Briganti-2019 nomogram (AUC 0.78 [95% CI 0.71–0.86] vs 0.80 [95% CI 0.73–0.86]; p = 0.76). The Amsterdam-Brisbane-Sydney nomogram showed excellent calibration on external validation, with an increased net benefit at a threshold probability of ≥4%. ConclusionsThe validated Amsterdam-Brisbane-Sydney nomogram performs superior to the Briganti-2017 and MSKCC nomograms, and similar to the Briganti-2019 nomogram. Furthermore, it is applicable in all patients with newly diagnosed unfavorable intermediate- and high-risk PCa. Patient summaryWe developed and validated the Amsterdam-Brisbane-Sydney nomogram for the prediction of prostate cancer spread to lymph nodes before surgery. This nomogram performs similar or superior to all presently available nomograms.

MP09-04 DIAGNOSTIC PERFORMANCE OF A NOVEL RADIOMIC MODEL FOR PREDICTING POST-TREATMENT PROSTATE CANCER RECURRENCE: A COMPARISON TO CAPRA AND MSKCC NOMOGRAMS

You have accessJournal of UrologyCME1 Apr 2023MP09-04 DIAGNOSTIC PERFORMANCE OF A NOVEL RADIOMIC MODEL FOR PREDICTING POST-TREATMENT PROSTATE CANCER RECURRENCE: A COMPARISON TO CAPRA AND MSKCC NOMOGRAMS Linda Huynh, Olivia Taylor, Shuo Wang, Thomas Ahlering, and Michael Baine Linda HuynhLinda Huynh More articles by this author , Olivia TaylorOlivia Taylor More articles by this author , Shuo WangShuo Wang More articles by this author , Thomas AhleringThomas Ahlering More articles by this author , and Michael BaineMichael Baine More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: mpMRI-derived radiomic features have been shown to capture sub-visual patterns for quantitative characterization of tumor phenotype. We seek to compare the diagnostic performance of a mpMRI-based radiomic model to currently available nomograms for prediction of post-radical prostatectomy (RP) biochemical recurrence (BCR). METHODS: mpMRI was obtained from 76 patients who had underwent RP for treatment of localized PCa. All patients had ≥2 years follow-up and those with neo-adjuvant or adjuvant treatment were excluded. Radiomic analysis and cross-validation of mpMRI features yielded features significantly correlated with BCR, defined as two consecutive serum PSA ≥0.2ng/ml. These features were aggregated to construct a radiomic model, which was compared to the risk scores generated by inputting patients’ clinicodemographic features into the USCF Cancer of the Prostate Risk Assessment (UCSF-CAPRA) score and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical Prostatectomy nomogram. The performance of each model was compared utilizing receiver-operator curve (ROC) analysis and area under the curve (AUC) was reported. RESULTS: Table 1 illustrates the clinicopathologic characteristics. In feature extraction and ranking, six radiomic features were determined to be important and non-redundant in predicting PCa recurrence (Figure 1). These features were aggregated into the radiomic model and repeated five-fold cross validation yielded a model with AUC of 0.95±0.06, 33% sensitivity, and 100% specificity. UCSF-CAPRA and MSKCC nomograms yielded AUC of 0.72±0.07 and 0.82±0.07, respectively. CONCLUSIONS: The mpMRI-derived radiomic model performed well when compared to the UCSF-CAPRA score and MSKCC Pre-Radical Prostatectomy nomogram. Future projects will incorporate patient demographics and disease characteristics available at the time of initial PCa diagnosis to improve the radiomic model accuracy. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e105 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Linda Huynh More articles by this author Olivia Taylor More articles by this author Shuo Wang More articles by this author Thomas Ahlering More articles by this author Michael Baine More articles by this author Expand All Advertisement PDF downloadLoading ...

Are the MIA and MSKCC nomograms useful in selecting patients with melanoma for sentinel lymph node biopsy?

The Melanoma Institute of Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms were developed to help guide sentinel lymph node biopsy (SLNB) decisions. Although statistically validated, whether these prediction models provide clinical benefit at National Comprehensive Cancer Network guideline-endorsed thresholds is unknown. We conducted a net benefit analysis to quantify the clinical utility of these nomograms at risk thresholds of 5%-10% compared to the alternative strategy of biopsying all patients. External validation data for MIA and MSKCC nomograms were extracted from respective published studies. The MIA nomogram provided added net benefit at a risk threshold of 9% but net harm at 5%-8% and 10%. The MSKCC nomogram provided added net benefit at risk thresholds of 5% and 9%-10% but net harm at 6%-8%. When present, the magnitude of net benefit was small (1-3 net avoidable biopsies per 100 patients). Neither model consistently provided added net benefit compared to performing SLNB for all patients. Based on published data, use of the MIA or MSKCC nomograms as decision-making tools for SLNB at risk thresholds of 5%-10% does not clearly provide clinical benefit to patients.

Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis.

The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients. The pathological features of 2,561 breast cancer patients were retrospectively reviewed, and the patients were divided into training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram. According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755. A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases.

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer

BackgroundPelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomogram are elegant and simple traditional tools used to estimate the risk of LNI and select patients for PLND. ObjectiveTo determine whether machine learning (ML) can improve patient selection and outperform currently available tools for predicting LNI using similar readily available clinicopathologic variables. Design, setting, and participantsRetrospective data for patients treated with surgery and PLND between 1990 and 2020 in two academic institutions were used. Outcome measurements and statistical analysisWe trained three models (two logistic regression models and one gradient-boosted trees–based model [XGBoost]) on data provided from one institution (n = 20267) with age, prostate-specific antigen (PSA) levels, clinical T stage, percentage positive cores, and Gleason scores as inputs. We externally validated these models using data from another institution (n = 1322) and compared their performance to that of the traditional models using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Results and limitationsLNI was present in 2563 patients (11.9%) overall, and in 119 patients (9%) in the validation data set. XGBoost had the best performance among all the models. On external validation, its AUC outperformed that of the Roach formula by 0.08 (95% confidence interval [CI] 0.042–0.12), the MSKCC nomogram by 0.05 (95% CI 0.016–0.070), and the Briganti nomogram by 0.03 (95% CI 0.0092–0.051; all p < 0.05). It also had better calibration and clinical utility in terms of net benefit on DCA across relevant clinical thresholds. The main limitation of the study is its retrospective design. ConclusionsTaking all measures of performance together, ML using standard clinicopathologic variables outperforms traditional tools in predicting LNI. Patient summaryDetermining the risk of cancer spread to the lymph nodes in patients with prostate cancer allows surgeons to perform lymph node dissection only in patients who need it and avoid the side effects of the procedure in those who do not. In this study, we used machine learning to develop a new calculator to predict the risk of lymph node involvement that outperformed traditional tools currently used by oncologists.

Preoperative predictive factors affecting sentinel lymph node positivity in breast cancer and comparison of their effectiveness with existing nomograms.

This study aimed to establish a strong regression model by revealing the preoperative predictive factors for sentinel lymph node (SLN) positivity in patients with early stage breast cancer (ESBC). In total, 445 patients who underwent SLN dissection for ESBC were included. All data that may be potential predictors of SLN positivity were retrospectively analyzed. Tumor size >2 cm, human epidermal growth factor receptor 2 (HER2) + status, lymphovascular invasion (LVI), palpable tumor, microcalcifications, multifocality or multicentricity, and axillary ultrasonographic findings were defined as independent predictors of SLN involvement. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.797, 0.808, and 0.870 for the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram, MD Anderson Cancer Center (MDACC) nomogram, and our regression model, respectively (P < .001). The recent model for predicting SLN status in ESBC was found to be stronger than existing nomograms. Parameters not included in current nomograms, such as palpable tumors, microcalcifications, and axillary ultrasonographic findings, are likely to make this model more meaningful.

MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer.

Objective The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade. Materials and Methods 105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme. Results The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models. Conclusion The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.

Predictive Value of Cribriform and Intraductal Carcinoma for the Nomogram-based Selection of Prostate Cancer Patients for Pelvic Lymph Node Dissection

ObjectiveTo assess the predictive value of biopsy-identified cribriform carcinoma and/or intraductal carcinoma (CR/IDC) within the Briganti and MSKCC nomograms predicting lymph node metastasis (LNM) in patients with primary prostate cancer (PCa). MethodsWe retrospectively included 393 PCa patients who underwent radical prostatectomy with extended pelvic lymph node dissection at 3 tertiary referral centers. We externally validated 2 prediction tools: the Briganti 2012 nomogram and the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram. Both nomograms were augmented with CR/IDC. The original model was compared with the CR/IDC-updated model using the likelihood ratio test. The performance of the prediction tools was assessed using calibration, discrimination, and clinical utility. ResultsOverall, 109 (28%) men were diagnosed with LNM. Calibration plots of the Briganti and MSKCC nomograms demonstrated an underestimation of the LNM risk across clinically relevant thresholds (≤15%). The addition of CR/IDC to the Briganti nomogram increased the fit of the data (χ2(1) = 4.30, P = .04), but did not improve the area under the curve (AUC) (0.69, 95% CI 0.63-0.75 vs 0.69, 95% CI 0.64-0.75). Incorporation of CR/IDC in the MSKCC nomogram resulted in an increased fit on the data (χ2(1) = 10.04, P <.01), but did not increase the AUC (0.66, 95% CI 0.60-0.72 vs 0.68, 95% CI 0.62-0.74). The addition of CR/IDC to the Briganti and MSKCC nomograms did not improve the clinical risk prediction. ConclusionIncorporation of CR/IDC into the 2 clinically most used pre-radical prostatectomy nomograms does not improve LNM prediction in a multinational, contemporary PCa cohort.

Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment.

To develop and test radiomics models based on manually corrected or automatically gained masks on ADC maps for pelvic lymph node metastasis (PLNM) prediction in patients with prostate cancer (PCa). A primary cohort of 474 patients with PCa who underwent prostate mpMRI were retrospectively enrolled for PLNM prediction between January 2017 and January 2020. They were then randomly split into training/validation (n = 332) and test (n = 142) groups for model development and internal testing. Four radiomics models were developed using four masks (manually corrected/automatic prostate gland and PCa lesion segmentation) based on the ADC maps using the primary cohort. Another cohort of 128 patients who underwent radical prostatectomy (RP) with extended pelvic lymph node dissection (ePLND) for PCa was used as the testing cohort between February 2020 and October 2021. The performance of the models was evaluated in terms of discrimination and clinical usefulness using the area under the curve (AUC) and decision curve analysis (DCA). The optimal radiomics model was further compared with Memorial Sloan Kettering Cancer Center (MSKCC) and Briganti 2017 nomograms, and PI-RADS assessment. 17 (13.28%) Patients with PLNM were included in the testing cohort. The radiomics model based on the mask of automatically segmented prostate obtained the highest AUC among the four radiomics models (0.73 vs. 0.63 vs. 0.70 vs. 0.56). Briganti 2017, MSKCC nomograms, and PI-RADS assessment-yielded AUCs of 0.69, 0.71, and 0.70, respectively, and no significant differences were found compared with the optimal radiomics model (P = 0.605-0.955). The radiomics model based on the mask of automatically segmented prostate offers a non-invasive method to predict PLNM for patients with PCa. It shows comparable accuracy to the current MKSCC and Briganti nomograms.

External validation of the MSKCC nomogram to estimate five-year overall survival after surgery for stage I–III colon cancer in a Dutch population

Introduction The Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram has been developed to estimate five-year overall survival (OS) after curative-intent surgery of colon cancer based on age, sex, T stage, differentiation grade, number of positive and examined regional lymph nodes. This is the first evaluation of the performance of the MSKCC model in a European population regarding prediction of OS. Material and methods Population-based data from patients with stage I–III colon cancer diagnosed between 2010 and 2016 were obtained from the Netherlands Cancer Registry (NCR) for external validation of the MSKCC prediction model. Five-year survival probabilities were estimated for all patients in our dataset by using the MSKCC prediction equation. Histogram density plots were created to depict the distribution of the estimated probability and prognostic index. The performance of the model was evaluated in terms of its overall performance, discrimination, and calibration. Results A total of 39,805 patients were included. Five-year OS was 71.9% (95% CI 71.5; 72.3) (11,051 events) with a median follow up of 5.6 years (IQR 4.1; 7.7). The Brier score was 0.10 (95% CI 0.10; 0.10). The C-index was 0.75 (95% CI 0.75; 0.76). The calibration measures and plot indicated that the model slightly overestimated observed mortality (observed/expected ratio = 0.86 [95% CI 0.86; 0.87], calibration intercept = −0.14 [95% CI −0.16; −0.11], and slope 1.07 [95% CI 1.05; 1.09], ICI = 0.04, E50 = 0.04, and E90 = 0.05). Conclusions The external validation of the MSKCC prediction nomogram in a large Dutch cohort supports the use of this practical tool in the European patient population. These personalised estimated survival probabilities may support clinicians when informing patients about prognosis. Adding potential relevant prognostic factors to the model, such as primary tumour location, might further improve the model.

POZİTİF SENTİNEL LENF DÜĞÜMÜ OLAN MEME KANSERLİ HASTALARDA TAMAMLAYICI AKSİLLER DİSEKSIYON GEREKLİLİĞİNİN NOMOGRAM YÖNTEMLERİNİN DEĞERLENDİRİLMESİYLE BELİRLENMESİ

AIM: Our aim was to evaluate the efficacies of different nomogram approaches, including the Memorial Sloan Kettering Cancer Centre (MSKCC), Stanford University, Tenon Hospital, Cambridge University and TR methods, in patients operated for breast cancer.&#x0D; MATERIAL AND METHOD: The study included 60 breast cancer patients who were operated on for breast cancer at Ankara Numune Training and Research Hospital, A2 General Surgery Clinic, between 2007 and 2012. All patients were sentinel lymph node (SLN)-positive and had undergone axillary lymph node dissection (ALND). Five different scoring systems were applied retrospectively to these 60 patients. Receiver operating characteristics (ROC) curves were created for the nomograms and the area under the curve (AUC) sensitivity and specificity results were calculated.&#x0D; RESULTS: Twenty-two patients (37%) had axillary non-SLN metastases. AUC values of MSKCC, Stanford, Cambridge, Tenon and TR nomogram were calculated as 0.646, 0.644, 0.62, 0.595, and 0.66, respectively. A significant difference was found between the groups with and without non-SLN metastasis in terms of SLN metastasis size (p = 0.013).&#x0D; CONCLUSION: The MSKCC, Stanford, Cambridge, Tenon, and TR nomogram models were found to have insufficient power to discriminate between patients with and without non-SLN metastases in patients with SLN-positive breast cancer (AUC values

External Validation of the Extraprostatic Extension Grade on MRI and Its Incremental Value to Clinical Models for Assessing Extraprostatic Cancer.

ObjectivesTo externally validate the extraprostatic extension (EPE) grade criteria on MRI and analyze the incremental value of EPE grade to clinical models of prostate cancer.MethodsA consecutive 130 patients who underwent preoperative prostate MRI followed by radical prostatectomy between January 2015 to January 2020 in our institution were retrospectively enrolled. The EPE grade, Cancer of the Prostate Risk Assessment (CAPRA), and Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) score for each patient were assigned. Significant clinicopathological factors in univariate and multivariate analyses were combined with EPE grade to build the Clinical + EPE grade model, and the CAPRA and MSKCCn score were also combined with EPE grade to build the CAPRA + EPE grade and MSKCCn + EPE grade model, respectively. The area under the curve (AUC), sensitivity and specificity of these models were calculated to evaluate their diagnostic performance. Calibration and decision curve analyses were used to analyze their calibration performance and clinical utility.ResultsThe AUC for predicting EPE was 0.767–0.778 for EPE grade, 0.704 for CAPRA, and 0.723 for MSKCCn. After combination with EPE grade, the AUCs of these clinical models increased significantly than using clinical models along (P < 0.05), but was comparable with using EPE grade alone (P > 0.05). The calibration curves of EPE grade, clinical models and combined models showed that these models are well-calibrated for EPE. In the decision curve analysis, EPE grade showed slightly higher net benefit than MSKCCn and CAPRA.ConclusionThe EPE grade showed good performance for evaluating EPE in our cohort and possessed well clinical utility. Further combinations with the EPE grade could improve the diagnostic performance of clinical models.

AB112. SOH21AS210. Predicting sentinel node positivity in patients with melanoma: which nomogram?

Background: Clinicopathological parameters can be used to estimate probability of sentinel nodal metastases in patients with malignant melanoma. The aim of this study is to compare the predictive values of the Memorial Sloan-Kettering Cancer Centre (MSKCC) nomogram and the newly developed Melanoma Institute Australia (MIA) nomogram to predict sentinel lymph node (SLN) metastases in patients with primary cutaneous melanoma.